An alternative derivation of Harville's restricted log likelihood function for variance component estimation

Estimation of variance components in linear mixed models is important in clinical trial and longitudinal data analysis. It is also important in animal and plant breeding for accurately partitioning total phenotypic variance into genetic and environmental variances. Restricted maximum likelihood (REML) method is often preferred over the maximum likelihood (ML) method for variance component estimation because REML takes into account the lost degree of freedom resulting from estimating the fixed effects. The original restricted likelihood function involves a linear transformation of the original response variable (a collection of error contrasts). Harville's final form of the restricted likelihood function does not involve the transformation and thus is much easier to manipulate than the original restricted likelihood function. There are several different ways to show that the two forms of the restricted likelihood are equivalent. In this study, I present a much simpler way to derive Harville's restricted likelihood function. I first treat the fixed effects as random effects and call such a mixed model a pseudo random model (PDRM). I then construct a likelihood function for the PDRM. Finally, I let the variance of the pseudo random effects be infinity and show that the limit of the likelihood function of the PDRM is the restricted likelihood function.     

Standard errors of heritabilities for forage breeding nurseries

Estimators for standard errors of heritability for two typical and complex forage breeding models are illustrated with estimates of variance and covariance components from least-squares and restricted maximum likelihood methods. The first experiment (Model 1) is a factorial design (two grazing management factors) with two layers of split plots (populations and grazing periods) and with plants within populations nested beneath the first split. The second model is for a sites pooling of randomized complete block experiments, with plants nested inside plots and with a split in time. One definition of heritability was applied to the plant habit character for both models in a red clover germplasm. Heritability estimates were statistically the same for the two methods for both models.     

Maximum likelihood estimation of oncogenetic tree models

We present a new approach for modelling the dependences between genetic changes in human tumours. In solid tumours, data on genetic alterations are usually only available at a single point in time, allowing no direct insight into the sequential order of genetic events. In our approach, genetic tumour development and progression is assumed to follow a probabilistic tree model. We show how maximum likelihood estimation can be used to reconstruct a tree model for the dependences between genetic alterations in a given tumour type. We illustrate the use of the proposed method by applying it to cytogenetic data from 173 cases of clear cell renal cell carcinoma, arriving at a model for the karyotypic evolution of this tumour.     

Maximum likelihood estimation of ordered multinomial parameters

The pool adjacent violator algorithm Ayer et al. (1955, The Annals of Mathematical Statistics, 26, 641-647) has long been known to give the maximum likelihood estimator of a series of ordered binomial parameters, based on an independent observation from each distribution (see Barlow et al., 1972, Statistical Inference under Order Restrictions, Wiley, New York). This result has immediate application to estimation of a survival distribution based on current survival status at a set of monitoring times. This paper considers an extended problem of maximum likelihood estimation of a series of 'ordered' multinomial parameters p(i)= (p(1i),p(2i),.,p(mi)) for 1 相似文献   

Multivariate apportionment of global human craniometric diversity

Extensive research in human genetics on presumably neutral loci has shown that the overwhelming majority of human diversity is found among individuals within local populations. Previous apportionments of craniometric diversity are similar to these genetic apportionments, implying that interregionally differing selection pressures have played a limited role in producing contemporary human cranial diversity. Here we show that when cranial variation is independently partitioned using principal components analysis of Mosimann shape variables, some estimates of interregional craniometric differentiation are larger than those for most genetic loci. These estimates are similar to estimates for genetic loci where interregionally differing selection pressures are implicated in producing the observed patterns of variation within and among regions. These results suggest that differences among regions in at least some cranial features, particularly in the nasal region, are in part the product of interregionally differing selection pressures. Moreover, these results have implications for assessing the biological affinities of prehistoric samples that are temporally separated from contemporary human reference populations.     

Maximum likelihood estimation in dynamical models of HIV

The study of dynamical models of HIV infection, based on a system of nonlinear ordinary differential equations (ODE), has considerably improved the knowledge of its pathogenesis. While the first models used simplified ODE systems and analyzed each patient separately, recent works dealt with inference in non-simplified models borrowing strength from the whole sample. The complexity of these models leads to great difficulties for inference and only the Bayesian approach has been attempted by now. We propose a full likelihood inference, adapting a Newton-like algorithm for these particular models. We consider a relatively complex ODE model for HIV infection and a model for the observations including the issue of detection limits. We apply this approach to the analysis of a clinical trial of antiretroviral therapy (ALBI ANRS 070) and we show that the whole algorithm works well in a simulation study.     

Fixed‐effect variance and the estimation of repeatabilities and heritabilities: issues and solutions

Linear mixed‐effects models are frequently used for estimating quantitative genetic parameters, including the heritability, as well as the repeatability, of traits. Heritability acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change, whereas repeatability informs us about the individual consistency of phenotypic traits. As quantities of biological interest, it is important that the denominator, the phenotypic variance in both cases, reflects the amount of phenotypic variance in the relevant ecological setting. The current practice of quantifying heritabilities and repeatabilities from mixed‐effects models frequently deprives their denominator of variance explained by fixed effects (often leading to upward bias of heritabilities and repeatabilities), and it has been suggested to omit fixed effects when estimating heritabilities in particular. We advocate an alternative option of fitting models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of the phenotypic variance. The approach is easily implemented and allows optimizing the estimation of phenotypic variance, for example by the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We address the estimation and interpretation of heritabilities in situations in which potential covariates are themselves heritable traits of the organism. Furthermore, we discuss complications that arise in generalized and nonlinear mixed models with fixed effects. In these cases, the variance parameters on the data scale depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.     

Maximum likelihood estimation of the kinetics of receptor-mediated adhesion

Adhesion flow assays are commonly employed to characterize the kinetics and force-dependence of receptor-ligand interactions. As transient cellular adhesion events are often mediated by a small number of receptor-ligand complexes (tether bonds) their durations are highly variable, which in turn presents obstacles to standard methods of analysis. In this paper, we employ the stochastic approach to chemical kinetics to construct the pause time distribution. Using this distribution, we develop a robust maximum likelihood (ML) approach to the robust estimation of rate constants associated with receptor-mediated transient adhesion and their confidence intervals. We then formulate robust estimators of the parameters of models for the force-dependence of the off-rate. Lastly, we develop a robust method of elucidation of the force-dependence of the off-rate using Akaike's information criterion (AIC). Our findings conclusively demonstrate that ML estimators of adhesion kinetics are substantial improvements over more conventional approaches, and when combined with Fisher information, they may be used to objectively and reproducibly distinguish the kinetics of different receptor-ligand complexes. Software for the implementation of these methods with experimental data is publicly available as for download at http://www.laurenzi.net.     

Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods

Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called the discrete gamma model, uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good, and four such categories appear to be sufficient to produce both an optimum, or near-optimum fit by the model to the data, and also an acceptable approximation to the continuous distribution. The second method, called fixed-rates model, classifies sites into several classes according to their rates predicted assuming the star tree. Sites in different classes are then assumed to be evolving at these fixed rates when other tree topologies are evaluated. Analyses of the data sets suggest that this method can produce reasonable results, but it seems to share some properties of a least-squares pairwise comparison; for example, interior branch lengths in nonbest trees are often found to be zero. The computational requirements of the two methods are comparable to that of Felsenstein's (1981, J Mol Evol 17:368–376) model, which assumes a single rate for all the sites.     

最大似然法和贝叶斯推论与似然比检验探讨泽泻目系统发育关系

应用最大似然法(ML)、贝叶斯推论(BI)、邻接法(NJ)和似然比检验(hLRTs)进行泽泻目分子系统学研究。所用的rbcL基因序列代表了泽泻目14科46属以及作为外类群的6相关属。研究结果表明,*等级制似然比检验表明泽泻目rbcL序列最适合的DNA进化模型为GTR+I+G,最大似然法、贝叶斯法和邻接法构建的系统发育树拓扑结构相似,没有显著的差异,但贝叶斯树支持率较高;泽泻目为一单系类群,由两个主要谱系分支构成,深层分布格局由5个主要分支构成。基于分子系统发育树,文中对泽泻目科间、水鳖科+茨藻科、泽泻科+花蔺科+黄花蔺科、和"Cymodoeaceae complex"的系统发育关系进行了讨论。研究结果还表明,泽泻目系统发育关系可能还需要更多的证据进一步的澄清。     

Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data

Two-level data with hierarchical structure and mixed continuous and polytomous data are very common in biomedical research. In this article, we propose a maximum likelihood approach for analyzing a latent variable model with these data. The maximum likelihood estimates are obtained by a Monte Carlo EM algorithm that involves the Gibbs sampler for approximating the E-step and the M-step and the bridge sampling for monitoring the convergence. The approach is illustrated by a two-level data set concerning the development and preliminary findings from an AIDS preventative intervention for Filipina commercial sex workers where the relationship between some latent quantities is investigated.     

Maximum likelihood estimation in random effects cure rate models with nonignorable missing covariates

We introduce a method of parameter estimation for a random effects cure rate model. We also propose a methodology that allows us to account for nonignorable missing covariates in this class of models. The proposed method corrects for possible bias introduced by complete case analysis when missing data are not missing completely at random and is motivated by data from a pair of melanoma studies conducted by the Eastern Cooperative Oncology Group in which clustering by cohort or time of study entry was suspected. In addition, these models allow estimation of cure rates, which is desirable when we do not wish to assume that all subjects remain at risk of death or relapse from disease after sufficient follow-up. We develop an EM algorithm for the model and provide an efficient Gibbs sampling scheme for carrying out the E-step of the algorithm.     

Latitudinal variation in wild populations of Drosophila melanogaster: heritabilities and reaction norms

Large amounts of genetic variation for wing length and wing area were demonstrated both within and between Drosophila melanogaster populations along a latitudinal gradient in South America. Wing length and wing area showed a strong positive correlation with latitude in both wild flies and laboratory-raised descendants. Large population differences were observed for heritability and coefficient of variation of these two traits, whereas relatively small population differences were found for development time, viability, pupal mortality, sex ratio and their norms of reaction to four developmental temperatures. No clear-cut latitudinal clines were established for these life-history characters. These results are discussed in the light of Bergmann's Rule and the relation between larval development and adult body size.